OpenRaiser/Intern-Atlas huggingface dataset:
Joint Detection and Tracking in Videos with Identification Features
Venue: arXiv cs.CV 2020
Year: 2020

Recent works have shown that combining object detection and tracking tasks, in the case of video data, results in higher performance for both tasks, but they require a high frame-rate as a strict requirement for performance. This is assumption is often violated in real-world applications, when models run on embedded devices, often at only a few frames per second. Videos at low frame-rate suffer from large object displacements. Here re-identification features may support to match large-displaced object detections, but current joint detection and re-identification formulations degrade the detector performance, as these two are contrasting tasks. In the real-world application having separate detector and re-id models is often not feasible, as both the memory and runtime effectively double. Towards robust long-term tracking applicable to reduced-computational-power devices, we propose the first joint optimization of detection, tracking and re-identification features for videos. Notably, our joint optimization maintains the detector performance, a typical multi-task challenge. At inference time, we leverage detections for tracking (tracking-by-detection) when the objects are visible, detectable and slowly moving in the image. We leverage instead re-identification features to match objects which disappeared (e.g. due to occlusion) for several frames or were not tracked due to fast motion (or low-frame-rate videos). Our proposed method reaches the state-of-the-art on MOT, it ranks 1st in the UA-DETRAC'18 tracking challenge among online trackers, and 3rd overall.
Succinct Trit-array Trie for Scalable Trajectory Similarity Search
Venue: arXiv cs.DS 2020
Year: 2020

Massive datasets of spatial trajectories representing the mobility of a diversity of moving objects are ubiquitous in research and industry. Similarity search of a large collection of trajectories is indispensable for turning these datasets into knowledge. Locality sensitive hashing (LSH) is a powerful technique for fast similarity searches. Recent methods employ LSH and attempt to realize an efficient similarity search of trajectories; however, those methods are inefficient in terms of search time and memory when applied to massive datasets. To address this problem, we present the trajectory-indexing succinct trit-array trie (tSTAT), which is a scalable method leveraging LSH for trajectory similarity searches. tSTAT quickly performs the search on a tree data structure called trie. We also present two novel techniques that enable to dramatically enhance the memory efficiency of tSTAT. One is a node reduction technique that substantially omits redundant trie nodes while maintaining the time performance. The other is a space-efficient representation that leverages the idea behind succinct data structures (i.e., a compressed data structure supporting fast data operations). We experimentally test tSTAT on its ability to retrieve similar trajectories for a query from large collections of trajectories and show that tSTAT performs superiorly in comparison to state-of-the-art similarity search methods.
Investigating Vulnerability to Adversarial Examples on Multimodal Data Fusion in Deep Learning
Venue: arXiv cs.CV 2020
Year: 2020

The success of multimodal data fusion in deep learning appears to be attributed to the use of complementary in-formation between multiple input data. Compared to their predictive performance, relatively less attention has been devoted to the robustness of multimodal fusion models. In this paper, we investigated whether the current multimodal fusion model utilizes the complementary intelligence to defend against adversarial attacks. We applied gradient based white-box attacks such as FGSM and PGD on MFNet, which is a major multispectral (RGB, Thermal) fusion deep learning model for semantic segmentation. We verified that the multimodal fusion model optimized for better prediction is still vulnerable to adversarial attack, even if only one of the sensors is attacked. Thus, it is hard to say that existing multimodal data fusion models are fully utilizing complementary relationships between multiple modalities in terms of adversarial robustness. We believe that our observations open a new horizon for adversarial attack research on multimodal data fusion.
AC Loop Current Attacks Against The KLJN Secure Key Exchange Scheme
Venue: arXiv cs.CR 2020
Year: 2020

A new attack against the Kirchhoff-Law-Johnson-Noise(KLJN) secure key exchange scheme is introduced. The attack exploits a parasitic (periodic) AC voltage-source at either Alice or Bob ends. Such situations exist due to AC ground loops and electromagnetic interference (EMI). In the low-frequency case, the procedure is the generalized form of the former DC ground loop based attack. In the high-frequency case, the power spectrum of the wire voltage is utilized. The attack is demonstrated in both the low and the high-frequency situations. Defense protocols against the attack are also discussed.
Target Location Problem for Multi-commodity Flow
Venue: arXiv cs.DS 2020
Year: 2020

Motivated by scheduling in Geo-distributed data analysis, we propose a target location problem for multi-commodity flow (LoMuF for short). Given commodities to be sent from their resources, LoMuF aims at locating their targets so that the multi-commodity flow is optimized in some sense. LoMuF is a combination of two fundamental problems, namely, the facility location problem and the network flow problem. We study the hardness and algorithmic issues of the problem in various settings. The findings lie in three aspects. First, a series of NP-hardness and APX-hardness results are obtained, uncovering the inherent difficulty in solving this problem. Second, we propose an approximation algorithm for general undirected networks and an exact algorithm for undirected trees, which naturally induce efficient approximation algorithms on directed networks. Third, we observe separations between directed networks and undirected ones, indicating that imposing direction on edges makes the problem strictly harder. These results show the richness of the problem and pave the way to further studies.
Vulnerability of deep neural networks for detecting COVID-19 cases from chest X-ray images to universal adversarial attacks
Venue: arXiv cs.CV 2020
Year: 2020

Under the epidemic of the novel coronavirus disease 2019 (COVID-19), chest X-ray computed tomography imaging is being used for effectively screening COVID-19 patients. The development of computer-aided systems based on deep neural networks (DNNs) has been advanced, to rapidly and accurately detect COVID-19 cases, because the need for expert radiologists, who are limited in number, forms a bottleneck for the screening. However, so far, the vulnerability of DNN-based systems has been poorly evaluated, although DNNs are vulnerable to a single perturbation, called universal adversarial perturbation (UAP), which can induce DNN failure in most classification tasks. Thus, we focus on representative DNN models for detecting COVID-19 cases from chest X-ray images and evaluate their vulnerability to UAPs generated using simple iterative algorithms. We consider nontargeted UAPs, which cause a task failure resulting in an input being assigned an incorrect label, and targeted UAPs, which cause the DNN to classify an input into a specific class. The results demonstrate that the models are vulnerable to nontargeted and targeted UAPs, even in case of small UAPs. In particular, 2% norm of the UPAs to the average norm of an image in the image dataset achieves >85% and >90% success rates for the nontargeted and targeted attacks, respectively. Due to the nontargeted UAPs, the DNN models judge most chest X-ray images as COVID-19 cases. The targeted UAPs make the DNN models classify most chest X-ray images into a given target class. The results indicate that careful consideration is required in practical applications of DNNs to COVID-19 diagnosis; in particular, they emphasize the need for strategies to address security concerns. As an example, we show that iterative fine-tuning of the DNN models using UAPs improves the robustness of the DNN models against UAPs.
Graph Degree Heterogeneity Facilitates Random Walker Meetings
Venue: arXiv cs.SI 2020
Year: 2020

Various graph algorithms have been developed with multiple random walks, the movement of several independent random walkers on a graph. Designing an efficient graph algorithm based on multiple random walks requires investigating multiple random walks theoretically to attain a deep understanding of their characteristics. The first meeting time is one of the important metrics for multiple random walks. The first meeting time on a graph is defined by the time it takes for multiple random walkers to meet at the same node in a graph. This time is closely related to the rendezvous problem, a fundamental problem in computer science. The first meeting time of multiple random walks has been analyzed previously, but many of these analyses have focused on regular graphs. In this paper, we analyze the first meeting time of multiple random walks in arbitrary graphs and clarify the effects of graph structures on expected values. First, we derive the spectral formula of the expected first meeting time on the basis of spectral graph theory. Then, we examine the principal component of the expected first meeting time using the derived spectral formula. The clarified principal component reveals that (a)the expected first meeting time is almost dominated by $n/(1+d_{\rm std}^2/d_{\rm avg}^2)$ and (b)the expected first meeting time is independent of the starting nodes of random walkers, where $n$ is the number of nodes of the graph. $d_{\rm avg}$ and $d_{\rm std}$ are the average and the standard deviation of weighted node degrees, respectively. The characteristic(a) is useful for understanding the effect of the graph structure on the first meeting time. According to the revealed effect of graph structures, the variance of the coefficient $d_{\rm std}/d_{\rm avg}$(degree heterogeneity) for weighted degrees facilitates the meeting of random walkers.
RUSSE'2020: Findings of the First Taxonomy Enrichment Task for the Russian language
Venue: arXiv cs.CL 2020
Year: 2020

This paper describes the results of the first shared task on taxonomy enrichment for the Russian language. The participants were asked to extend an existing taxonomy with previously unseen words: for each new word their systems should provide a ranked list of possible (candidate) hypernyms. In comparison to the previous tasks for other languages, our competition has a more realistic task setting: new words were provided without definitions. Instead, we provided a textual corpus where these new terms occurred. For this evaluation campaign, we developed a new evaluation dataset based on unpublished RuWordNet data. The shared task features two tracks: "nouns" and "verbs". 16 teams participated in the task demonstrating high results with more than half of them outperforming the provided baseline.
GeoCoV19: A Dataset of Hundreds of Millions of Multilingual COVID-19 Tweets with Location Information
Venue: arXiv cs.SI 2020
Year: 2020

The past several years have witnessed a huge surge in the use of social media platforms during mass convergence events such as health emergencies, natural or human-induced disasters. These non-traditional data sources are becoming vital for disease forecasts and surveillance when preparing for epidemic and pandemic outbreaks. In this paper, we present GeoCoV19, a large-scale Twitter dataset containing more than 524 million multilingual tweets posted over a period of 90 days since February 1, 2020. Moreover, we employ a gazetteer-based approach to infer the geolocation of tweets. We postulate that this large-scale, multilingual, geolocated social media data can empower the research communities to evaluate how societies are collectively coping with this unprecedented global crisis as well as to develop computational methods to address challenges such as identifying fake news, understanding communities' knowledge gaps, building disease forecast and surveillance models, among others.
Semi-supervised Medical Image Classification with Global Latent Mixing
Venue: arXiv cs.LG 2020
Year: 2020

Computer-aided diagnosis via deep learning relies on large-scale annotated data sets, which can be costly when involving expert knowledge. Semi-supervised learning (SSL) mitigates this challenge by leveraging unlabeled data. One effective SSL approach is to regularize the local smoothness of neural functions via perturbations around single data points. In this work, we argue that regularizing the global smoothness of neural functions by filling the void in between data points can further improve SSL. We present a novel SSL approach that trains the neural network on linear mixing of labeled and unlabeled data, at both the input and latent space in order to regularize different portions of the network. We evaluated the presented model on two distinct medical image data sets for semi-supervised classification of thoracic disease and skin lesion, demonstrating its improved performance over SSL with local perturbations and SSL with global mixing but at the input space only. Our code is available at https://github.com/Prasanna1991/LatentMixing.
Mining Message Flows from System-on-Chip Execution Traces
Venue: arXiv cs.SE 2020
Year: 2020

Comprehensive and well-defined specifications are necessary to perform rigorous and thorough validation of system-on-chip (SoC) designs. Message flows specify how components of an SoC design communicate and coordinate with each other to realize various system functions. Message flow specifications are essential for efficient system-level validation and debug for SoC designs. However, in practice such specifications are usually not available, often ambiguous, incomplete, or even contain errors. This paper addresses that problem by proposing a specification mining framework, FlowMiner, that automatically extracts message flows from SoC execution traces, which, unlike software traces, show a high degree of concurrency. A set of inference rules and optimization techniques are presented to improve mining performance and reduce mining complexity. Evaluation of this framework in several experiments shows promising results.
OBDA for the Web: Creating Virtual RDF Graphs On Top of Web Data Sources
Venue: arXiv cs.DB 2020
Year: 2020

Due to Variety, Web data come in many different structures and formats, with HTML tables and REST APIs (e.g., social media APIs) being among the most popular ones. A big subset of Web data is also characterised by Velocity, as data gets frequently updated so that consumers can obtain the most up-to-date version of the respective datasets. At the moment, though, these data sources are not effectively supported by Semantic Web tools. To address variety and velocity, we propose Ontop4theWeb, a system that maps Web data of various formats into virtual RDF triples, thus allowing for querying them on-the-fly without materializing them as RDF. We demonstrate how Ontop4theWeb can use SPARQL to uniformly query popular, but heterogeneous Web data sources, like HTML tables and Web APIs. We showcase our approach in a number of use cases, such as Twitter, Foursquare, Yelp and HTML tables. We carried out a thorough experimental evaluation which verifies the high efficiency of our framework, which goes beyond the current state-of-the-art in this area, in terms of both functionality and performance.
Matrix moments of the diffusion tensor distribution
Venue: arXiv cs.CE 2020
Year: 2020

Purpose: To facilitate the implementation/validation of signal representations and models using parametric matrix-variate distributions to approximate the diffusion tensor distribution (DTD) $\mathcal{P}(\mathbf{D})$. Theory: We establish practical mathematical tools, the matrix moments of the DTD, enabling to compute the mean diffusion tensor and covariance tensor associated with any parametric matrix-variate DTD whose moment-generating function is known. As a proof of concept, we apply these tools to the non-central matrix-variate Gamma (nc-mv-Gamma) distribution, whose covariance tensor was so far unknown, and design a new signal representation capturing intra-voxel heterogeneity via a single nc-mv-Gamma distribution: the matrix-variate Gamma approximation. Methods: Furthering this proof of concept, we evaluate the matrix-variate Gamma approximation in silico and in vivo, in a human-brain 'tensor-valued' diffusion MRI dataset. Results: The matrix-variate Gamma approximation fails to capture the heterogeneity arising from orientation dispersion and from simultaneous variances in the trace (size) and anisotropy (shape) of the underlying diffusion tensors, which is explained by the structure of the covariance tensor associated with the nc-mv-Gamma distribution. Conclusion: The matrix moments promote a more widespread use of matrix-variate distributions as plausible approximations of the DTD by alleviating their intractability, thereby facilitating the design/validation of matrix-variate microstructural techniques.
The Fallibility of Contact-Tracing Apps
Venue: arXiv cs.CY 2020
Year: 2020

Since the onset of the COVID-19's global spread we have been following the debate around contact tracing apps -- the tech-enabled response to the pandemic. As corporations, academics, governments, and civil society discuss the right way to implement these apps, we noticed recurring implicit assumptions. The proposed solutions are designed for a world where Internet access and smartphone ownership are a given, people are willing and able to install these apps, and those who receive notifications about potential exposure to the virus have access to testing and can isolate safely. In this work we challenge these assumptions. We not only show that there are not enough smartphones worldwide to reach required adoption thresholds but also highlight a broad lack of internet access, which affects certain groups more: the elderly, those with lower incomes, and those with limited ability to socially distance. Unfortunately, these are also the groups that are at the highest risks from COVID-19. We also report that the contact tracing apps that are already deployed on an opt-in basis show disappointing adoption levels. We warn about the potential consequences of over-extending the existing state and corporate surveillance powers. Finally, we describe a multitude of scenarios where contact tracing apps will not help regardless of access or policy. In this work we call for a comprehensive and equitable policy response that prioritizes the needs of the most vulnerable, protects human rights, and considers long term impact instead of focusing on technology-first fixes.
Java Decompiler Diversity and its Application to Meta-decompilation
Venue: arXiv cs.SE 2020
Year: 2020

During compilation from Java source code to bytecode, some information is irreversibly lost. In other words, compilation and decompilation of Java code is not symmetric. Consequently, decompilation, which aims at producing source code from bytecode, relies on strategies to reconstruct the information that has been lost. Different Java decompilers use distinct strategies to achieve proper decompilation. In this work, we hypothesize that the diverse ways in which bytecode can be decompiled has a direct impact on the quality of the source code produced by decompilers. In this paper, we assess the strategies of eight Java decompilers with respect to three quality indicators: syntactic correctness, syntactic distortion and semantic equivalence modulo inputs. Our results show that no single modern decompiler is able to correctly handle the variety of bytecode structures coming from real-world programs. The highest ranking decompiler in this study produces syntactically correct, and semantically equivalent code output for 84%, respectively 78%, of the classes in our dataset. Our results demonstrate that each decompiler correctly handles a different set of bytecode classes. We propose a new decompiler called Arlecchino that leverages the diversity of existing decompilers. To do so, we merge partial decompilation into a new one based on compilation errors. Arlecchino handles 37.6% of bytecode classes that were previously handled by no decompiler. We publish the sources of this new bytecode decompiler.
Gleason Grading of Histology Prostate Images through Semantic Segmentation via Residual U-Net
Venue: arXiv cs.CV 2020
Year: 2020

Worldwide, prostate cancer is one of the main cancers affecting men. The final diagnosis of prostate cancer is based on the visual detection of Gleason patterns in prostate biopsy by pathologists. Computer-aided-diagnosis systems allow to delineate and classify the cancerous patterns in the tissue via computer-vision algorithms in order to support the physicians' task. The methodological core of this work is a U-Net convolutional neural network for image segmentation modified with residual blocks able to segment cancerous tissue according to the full Gleason system. This model outperforms other well-known architectures, and reaches a pixel-level Cohen's quadratic Kappa of 0.52, at the level of previous image-level works in the literature, but providing also a detailed localisation of the patterns.
A First Step Towards Content Protecting Plagiarism Detection
Venue: arXiv cs.CR 2020
Year: 2020

Plagiarism detection systems are essential tools for safeguarding academic and educational integrity. However, today's systems require disclosing the full content of the input documents and the document collection to which the input documents are compared. Moreover, the systems are centralized and under the control of individual, typically commercial providers. This situation raises procedural and legal concerns regarding the confidentiality of sensitive data, which can limit or prohibit the use of plagiarism detection services. To eliminate these weaknesses of current systems, we seek to devise a plagiarism detection approach that does not require a centralized provider nor exposing any content as cleartext. This paper presents the initial results of our research. Specifically, we employ Private Set Intersection to devise a content-protecting variant of the citation-based similarity measure Bibliographic Coupling implemented in our plagiarism detection system HyPlag. Our evaluation shows that the content-protecting method achieves the same detection effectiveness as the original method while making common attacks to disclose the protected content practically infeasible. Our future work will extend this successful proof-of-concept by devising plagiarism detection methods that can analyze the entire content of documents without disclosing it as cleartext.